A cloud is made of billows upon billows upon billows that look like clouds. As you come closer to a cloud you don't get something smooth, but irregularities at a smaller scale.
—Dr. Benoit Mandelbrot
One of the most robust tools for generating positive expectancy models is timeframe analysis. This chapter explores both traditional timeframe analysis and timeframe divergence. Particular emphasis is placed on using various mathematical technical indicators to understand likely market behavior in various timeframes.
TRADITIONAL TIMEFRAME ANALYSIS
I began studying market behavior in 1987 in the hope of developing positive expectancy trading models, and gravitated to the simplicity—when compared to fundamental analysis—of technical indicators. I began to understand the importance of market trends and that various technical indicators could help in trend identification. When struggling to determine the trend, I quickly realized that not all indicators were created equal, and that technical indicators derived from mathematical formulas offered an objective answer to the question “What is the trend?” As I continued studying price history, applying different mathematical technical indicators to determine the trend, I eventually learned that there was no single answer to the question. The only satisfactory answer to the question is another question: “What is your timeframe?”
In other words, according to objective mathematical technical indicators like moving ...